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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Twinac: initiation of a community-driven accelerator digital twin framework

We present the initiation of a community-driven framework for the integration of accelerator digital twins into control systems: Twinac. Few facilities have fully integrated accelerator digital twins like at Cornell’s CHESS. Many facilities have active research to employ surrogate models to aid in operational decisions like at Argonne’s ALS, MSU’s FRIB, SLAC’s LCLS-II, and Fermilab’s FAST/IOTA, PIP-II, and main complex. To lower the barrier to entry for all accelerator facilities to build and benefit from a digital twin of their own accelerators, we propose the following software framework. Twinac will provide the capability to compose one’s own digital twin using reusable components engineered at other facilities. With this model in place, Twinac will also support tools for (1) predictive maintenance systems; (2) discovery of correlated but uncontrolled environmental factors, like seasonal temperature variations causing performance changes on power supplies, magnets, etc.; and (3) prototyping and updating sophisticated optimization and controls algorithms. The Twinac framework will enable sharing and simplified deployment of modeled components and control algorithms at all facilities. With an inter-facility team to build and support the Twinac framework, it will be easy to publish and try out the latest advancements at one’s own facility.

Miceli, Tia [Fermilab]↗
DOE OSTI2020

A Guide to Energy Master Planning of High-Performance Districts and Communities

This guide was developed with partners throughout the United States to demonstrate how implementing district-scale high-performance strategies can be successful and scalable approaches to achieving deep energy savings that increase affordability, improve resilience, reduce emissions, and foster economic development. This document serves as a framework for districts, campuses, and communities, illustrating an iterative process of building support for, planning, and implementing high-performance districts by engaging stakeholders, setting aggressive energy goals, completing technical and financial planning, and implementing a high-performance energy master plan. The information in this guide is based on a 3-year U.S. Department of Energy Zero Energy District Accelerator and a range of real-world examples of emerging high-performance districts. It is particularly useful for architects, planners, engineers, local government agencies, and real estate developers in the early phases of planning a district with high-performance or other deep energy goals. For the purposes of this guide, a high-performance district is a multibuilding development that achieves aggressive energy and related goals such as zero energy, carbon neutrality, sustainability, ultra-efficiency, etc. High-performance districts optimize energy efficiency to reduce energy loads and use renewable energy resources to meet the remaining loads whenever possible. The Energy Independence and Security Act of 2007 defines a high-performance building as “a building that integrates and optimizes on a life cycle basis all major high-performance attributes, including energy conservation, environment, safety, security, durability, accessibility, cost-benefit, productivity, sustainability, functionality, and operational considerations.” High-performance districts are collections of such buildings that take advantage of the synergies available when energy consumption and production are considered at a district level rather than one building at a time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

FEM Modeling and Simulation of 2-D High Specific Heat Nb 3 Sn Wires

In the past few years, new high specific heat Nb 3 Sn wires have gained much focus at FNAL. Indeed, they have proved to be more stable against thermal perturbations with respect to standard wires. Nevertheless, a trade off exists between their thermal efficiency and production feasibility. In this report I describe the thermal and structural models that I have developed by exploiting ANSYS Mechanical APDL ® , which I got acquainted with at the beginning of my training. The aim has been to optimize the location of high specific heat elements in order to obtain an optimal thermal stability, while minimizing the risk of wire breakage during drawing. FEM results are compared with experimental ones made beforehand. Other minimum quench energy (MQE) thermal models have been developed for expected new experimental results. New data may improve the understanding of the embedded physical uncertainties in the model.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2023

Auto Stop-Start Fuel Consumption Benefits

With increasingly stringent regulations mandating the improvement of vehicle fuel economy, automotive manufacturers face growing pressure to develop and implement technologies that improve overall system efficiency. One such technology is an automatic (auto) stop-start feature. Auto stop-start reduces idle time and reduces fuel use by temporarily shutting the engine off when the vehicle comes to a stop and automatically re-starting it when the brake is released, or the accelerator is pressed. As mandated by the U.S. Congress, the U.S. Environmental Protection Agency (EPA) is required to keep the public informed about fuel saving practices. This is done, in partnership with the U.S. Department of Energy (DOE), through the fueleconomy.gov website. The “Fuel-Saving Technologies” and “Gas Mileage Tips” sections of the website are focused on helping the public make informed purchasing decisions and encouraging fuel-saving driving habits. Here, in order to provide users with accurate information about the auto stop-start feature, experiments were conducted to determine its fuel economy effect. Four vehicles were tested both with and without the feature enabled under three test cycles: the Federal Test Procedure (FTP) city fuel economy test, the US06 high acceleration aggressive driving schedule that is often identified as the “Supplemental FTP” driving schedule, and the EPA New York City Cycle (NYCC). The results were compared to measure the fuel economy and consumption effects of using the auto stop-start feature. It was found that the fuel economy improvement varied significantly between drive cycles depending on the amount and percentage of idle time during the test. The largest fuel economy improvements were 7.27% and 26.4% for the FTP and NYCC, respectively.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Application of Geant4 Monte Carlo simulation in dose calculations for small radiosurgical fields

The Geant4 toolkit was used to develop a Monte Carlo (MC)-based engine for accurate dose calculations in small radiation field sizes. The Geant4 toolkit (version 10.1.p02) was used to simulate 6-MV photon beam of a Varian2100C linear accelerator that is being used for stereotactic radiosurgery (SRS) treatment with small radiation fields. Geometric models of 3 in-house designed radiosurgical divergent cones, with the diameters of their projections at the isocenter being 10, 20, and 30 mm, were simulated. The accuracy of the MC simulation technique was examined by reproducing several different simulated dosimetric parameters of the primary beams with the experimental data. The dose distributions are first checked for single beams for each cone, then standard multiple field (SMF) techniques are applied. A sample set of DICOM files from computed tomography (CT) scan imaging of a patient's head was converted to the Geant4 geometry format to implement MC-based engine for a clinical test. To validate the accuracy of the MC-based calculations for SMF arrangements, the isodose lines from MC simulation in water phantom were compared with the measured isodose lines using EBT3 Gafchromic film in Solid Water phantoms. Agreements between measured and simulated depth dose values and beam profiles for SRS cones were generally within 2%/2 mm. For output factors, the largest discrepancy was observed for 10 mm SRS cone, which was 1.7%. For SMF techniques, in SRS cones, the MC simulation and EBT3 Gafchromic film dosimetry were in acceptable agreement (5%/5 mm). Excellent agreement between the results of the MC-based and measured dose values for both single and SMF techniques in SRS cones indicates the ability of the Geant4 toolkit to be applied as the platform for treatment planning of advanced radiotherapy techniques.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Comparing total cost of ownership of battery electric vehicles and internal combustion engine vehicles

The technological advance of electrochemical energy storage and the electric powertrain has led to rapid growth in the deployment of electric vehicles. The high cost and the added weight of the batteries have limited the size (energy storage capacity) and, therefore, the driving range of these vehicles. However, consumers are steadily purchasing these vehicles because of the fast acceleration, quiet ride, and high energy efficiency. The higher pack-to-wheel efficiency and the lower energy cost per mile, as well as the lower expense for maintenance and repair, translate to operating savings over conventional vehicles. Here we compare battery electric vehicles with internal combustion engine vehicles based on the total cost of ownership. It is seen that the higher initial cost of electric vehicles can be recovered in as little as 5 years. This is especially true for electric vehicles with shorter driving ranges. Specifically, a vehicle with an electric driving range under 200 miles may achieve cost parity with an equivalent internal combustion engine vehicle in 8 years or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Report on the Integration of Experimental and Modeling Data for Initial Equivalence Study of Mechanical Performance in Irradiated LPBF 316 Stainless Steel

To ensure the rapid development, deployment, and use of advanced nuclear technologies, faster qualification approaches are needed. Typically, the primary pathway uses traditional data packages consistent with the American Society of Mechanical Engineers Boiler and Pressure Vessel Code, which does not consider the environmental effects the material will experience such as corrosion and radiation damage. Examining radiation effects requires a significant amount of space in US facilities at the Advanced Test Reactor and the High Flux Isotope Reactor (HFIR) and suffers from natural gradients in temperature and neutron flux profiles. Ion irradiation may enable rapid assessment of radiation-induced damage to a material and is proposed as part of an accelerated materials qualification framework through the Advanced Materials and Manufacturing Technologies program. To enhance the utility of ion irradiation as an examination tool, this report provides the initial assessment of engineering-relevant properties of microstructures produced from ion irradiation in the near-surface volume. Nanoindentation, Vickers hardness, and known tensile properties were brought together with simple mathematical models and experimental data for an initial equivalence study of the mechanical performance of irradiated laser powder bed fusion (LPBF) 316 stainless steels across length scales. Direct observation of the calculated ion irradiation yield stress and measured neutron irradiation yield stress at 2 dpa showed that both datasets exhibit the same trend with irradiation temperature and overlap within an acceptable band of stress values. Ion irradiations at 10 dpa serve as a prediction of properties to compare to postirradiation examination of HFIR-irradiated LPBF 316H further in the program. This work is a significant demonstration of the Licensing Approach with Ions and Neutrons, which uses ion irradiations to generate mechanical property information more rapidly than through neutron irradiations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Neural message-passing for objective-based uncertainty quantification and optimal experimental design

Various real-world scientific applications involve the mathematical modeling of complex uncertain systems with numerous unknown parameters. Accurate parameter estimation is often practically infeasible in such systems, as the available training data may be insufficient and the cost of acquiring additional data may be high. In such cases, based on a Bayesian paradigm, we can design robust operators retaining the best overall performance across all possible models and design optimal experiments that can effectively reduce uncertainty to enhance the performance of such operators maximally. While objective-based uncertainty quantification (objective-UQ) based on MOCU (mean objective cost of uncertainty) provides an effective means for quantifying uncertainty in complex systems, the high computational cost of estimating MOCU has been a challenge in applying it to real-world scientific/engineering problems. In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach. We adopt a neural message-passing model for surrogate modeling, incorporating a novel axiomatic constraint loss that penalizes an increase in the estimated system uncertainty. As an illustrative example, we consider the optimal experimental design (OED) problem for uncertain Kuramoto models, where the goal is to predict the experiments that can most effectively enhance robust synchronization performance through uncertainty reduction. We show that our proposed approach can accelerate MOCU-based OED by four to five orders of magnitude, without any visible performance loss compared to the state-of-the-art. The proposed approach applies to general OED tasks, beyond the Kuramoto model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An MLIR-based Compiler Flow for System-Level Design and Hardware Acceleration

The generation of custom hardware accelerators for applications implemented within high-level productive programming frameworks requires considerable manual effort. To automate this process, we introduce \sodaopt, a compiler tool that extends the MLIR infrastructure. \sodaopt automatically searches, outlines, tiles, and pre-optimizes relevant code regions to generate high-quality accelerators through high-level synthesis. \sodaopt can support any high-level programming framework and domain-specific language that interface with the MLIR infrastructure. By leveraging MLIR, \sodaopt solves compiler optimization problems with specialized abstractions. Backend synthesis tools connect to \sodaopt through progressive intermediate representation lowerings. \sodaopt interfaces to a design space exploration engine to identify the combination of compiler optimization passes and options that provides high-performance generated designs for different backends and targets. We demonstrate the practical applicability of the compilation flow by exploring the automatic generation of accelerators for deep neural networks operators outlined at arbitrary granularity and by combining outlining with tiling on large convolution layers. Experimental results with kernels from the PolyBench benchmark show that \sodaopt high-level optimizations improve execution delays of synthesized accelerators up to 60x. We also show that for the selected kernels, our solution outperforms the current of state-of-the art in more than 70% of the benchmarks and provides better average speedup in 55% of them.

Bohm Agostini, Nicolas↗
DOE OSTI2024

Self-similar Reynolds-averaged mechanical–scalar turbulence models for Rayleigh–Taylor mixing induced by power-law accelerations in the small Atwood number limit

Analytical self-similar solutions to two-, three-, and four-equation Reynolds-averaged mechanical–scalar turbulence models describing turbulent Rayleigh–Taylor mixing driven by a temporal power-law acceleration are derived in the small Atwood number (Boussinesq) limit. The solutions generalize those previously derived for constant acceleration Rayleigh–Taylor mixing for models based on the turbulent kinetic energy K and its dissipation rate ε, together with the scalar variance S and its dissipation rate χ [O. Schilling, “Self-similar Reynolds-averaged mechanical–scalar turbulence models for Rayleigh–Taylor, Richtmyer–Meshkov, and Kelvin–Helmholtz instability-induced mixing in the small Atwood number limit,” Phys. Fluids 33, 085129 (2021)]. The turbulent fields are expressed in terms of the model coefficients and power-law exponent, with their temporal power-law scalings obtained by requiring that the self-similar equations are explicitly time-independent. Mixing layer growth parameters and other physical observables are obtained explicitly as functions of the model coefficients and parameterized by the exponent of the power-law acceleration. Values for physical observables in the constant acceleration case are used to calibrate the two-, three-, and four-equation models, such that the self-similar solutions are consistent with experimental and numerical simulation data corresponding to a canonical (i.e., constant acceleration) Rayleigh–Taylor turbulent flow. The calibrated four-equation model is then used to numerically reconstruct the mean and turbulent fields, and turbulent equation budgets across the mixing layer for several values of the power-law exponent. Finally, the reference solutions derived here can be used to understand the model predictions for strongly accelerated or decelerated Rayleigh–Taylor mixing in the large Reynolds number limit.

42 ENGINEERING↗
DOE OSTI2020

Fabricated ecosystem workshop: bridging laboratory to field science. Workshop report.

Lawrence Berkeley National Laboratory (Berkeley Lab) scientists held a workshop at the DOE BER Genomic Sciences PI meeting on the use of Fabricated Ecosystems in Washington D.C. on February 25th, 2020. Ecosystem fabrication is an approach to creating controlled microbial, soil and plant ecologies within a laboratory setting that enable discovery and dissection of environmental variables, activities, and interactions. At Berkeley Lab, we are developing two systems which span spatial and temporal scales — the EcoFAB and the EcoPOD. The participants of the workshop discussed the potential applications and challenges of using fabricated ecosystems as tools to tackle BER-relevant scientific questions. Specifically, they identified (1) research challenges that would benefit from ready access to this infrastructure (2) identified and prioritized technical challenges that currently limit these systems and (3) discussed how to use them to bridge the gap between lab and field research. To ensure that the fabricated ecosystems , in particular the larger-scale EcoPODs, are of use to as much of the BER community as possible, the workshop participants made the following recommendations: 1. Produce publicly available datasets for a set of control variables. Use for benchmarking, and assessing reproducibility between systems. 2. Implement data standards. 3. Develop/leverage nano/micro sensors and in situ root imaging. 4. Encourage the development of interdisciplinary teams to develop EcoPOD experiments. 5. Develop fabricated ecosystems with size and complexity that sits between the EcoFAB and EcoPOD to accelerate use and access.

42 ENGINEERING↗
DOE OSTI2020

Modeling Time-Dependent Surrogates of Additive-Manufactured Nuclear Fuels Processes

Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas for its ability to rapidly produce, prototype, and customize designs. Recently, a hybrid AM technique was successfully developed at Idaho National Laboratory (INL) to manufacture nuclear fuels [1]. Despite the advantages, this AM technique needs optimization due to defects from a highly complex melting and sintering process. The complex metallurgical phenomena during AM processes are strongly related to parameters such as applied laser power, traveling speed, and scan style, which could lead to differences in density, residual stress, crystallographic texture, and mechanical properties. In addition, stochastic variations in laser energy interaction and associated multiscale/multiphysics phenomena cause variations in microstructure evolution and mechanical properties. Currently, researchers at INL are focusing on developing a comprehensive modeling framework, leveraging INL’s simulation tools MOOSE/MARMOT/BISON/RAVEN [2-4] to describe all steps of this AM process across multiple length scales. Although this advanced framework plays a critical role in enabling enhancements to traditional trial and error approaches for design and optimization of nuclear fuel materials, it remains computationally intense, limiting its use in sensitivity and optimization analysis. In this case, an accurate and inexpensive surrogate becomes an effective tool for providing a tractable approximation of the underlying underline physics. Surrogate models generally not based on the physics of a system are purely mathematical models used to capture the relationships between specific system inputs and outputs. Popular approaches, including neural networks [5], response surfaces [6], and subspace-based reduced order models [7], have been applied to a wide range of disciplines, such as nuclear reactor design, aerospace design and automotive design. In this summary, we employ advanced time-dependent surrogate models such as high-dimensional model representation (HDMR) [8] and physics-informed deep neural network (PINNs) [9] to accelerate the design and optimization of AM process.

42 ENGINEERING↗
DOE OSTI2020

Cold plate with combined inclined impingement and ribbed channels

Heat transfer devices and methods for making the same that include a first enclosure having at least one inlet port; a second enclosure having a bottom plate and one or more dividing walls to establish channels, at least one internal surface of each channel having rib structures to create turbulence in a fluid flow; and a jet plate connecting the first enclosure and the second enclosure having impinging jets that convey fluid from the first enclosure to the channels, said impinging jets being set at an angular deviation from normal to cause local acceleration of fluid and to increase a local heat transfer rate.

42 ENGINEERING↗
DOE OSTI2025

Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios

This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.

De Souza, Reubun [School of Electrical Engineering↗
DOE OSTI2021

Morphogene-assisted transformation of Sorghum bicolor allows more efficient genome editing

Sorghum bicolor (L.) Moench, the fifth most important cereal worldwide, is a multi-use crop for feed, food, forage and fuel. To enhance the sorghum and other important crop plants, establishing gene function is essential for their improvement. For sorghum, identifying genes associated with its notable abiotic stress tolerances requires a detailed molecular understanding of the genes associated with those traits. The limits of this knowledge became evident from our earlier in-depth sorghum transcriptome study showing that over 40% of its transcriptome had not been annotated. Here, we describe a full spectrum of tools to engineer, edit, annotate and characterize sorghum’s genes. Efforts to develop those tools began with a morphogene-assisted transformation (MAT) method that led to accelerated transformation times, nearly half the time required with classical callus-based, non-MAT approaches. These efforts also led to expanded numbers of amenable genotypes, including several not previously transformed or historically recalcitrant. Another transformation advance, termed altruistic, involved introducing a gene of interest in a separate Agrobacterium strain from the one with morphogenes, leading to plants with the gene of interest but without morphogenes. The MAT approach was also successfully used to edit a target exemplary gene, phytoene desaturase. To identify single-copy transformed plants, we adapted a high-throughput technique and also developed a novel method to determine transgene independent integration. These efforts led to an efficient method to determine gene function, expediting research in numerous genotypes of this widely grown, multi-use crop.

54 ENVIRONMENTAL SCIENCES↗